exaone 3.0 7.8b it needs ~16.9 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~109 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
109.2 tok/s
TTFT
1773 ms
Safe context
929K
Memory
16.9 GB / 69.1 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 109.2 tok/s | 967 ms | 929K |
| Coding | C | Runs well | 109.2 tok/s | 1773 ms | 929K |
| Agentic Coding | C | Runs well | 109.2 tok/s | 2579 ms | 929K |
| Reasoning | C | Runs well | 109.2 tok/s | 2095 ms | 929K |
| RAG | C | Runs well | 109.2 tok/s | 3223 ms | 929K |
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | D40 |
Q3_K_S | 3 | 3.8 GB | Low | D40 |
NVFP4 | 4 |
Copy-paste commands to run exaone 3.0 7.8b it on your machine.
Run
lms load hf-bingsu--exaone-3-0-7-8b-it && lms server startYes, Mac Studio M3 Ultra 96GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 109.2 tok/s.
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 16.9 GB of memory with Q4_K_M quantization.
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 96GB, exaone 3.0 7.8b it achieves approximately 109.2 tokens per second decode speed with a time-to-first-token of 1773ms using Q4_K_M quantization.
For coding workloads, exaone 3.0 7.8b it on Mac Studio M3 Ultra 96GB receives a C grade with 109.2 tok/s and 929K context.
On Mac Studio M3 Ultra 96GB, exaone 3.0 7.8b it can safely use up to 929K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bingsu--exaone-3-0-7-8b-it-on-m3-ultra-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.4 GB |
| Medium |
| D40 |
Q4_K_M | 4 | 4.8 GB | Medium | D40 |
Q5_K_M | 5 | 5.6 GB | High | D40 |
Q6_K | 6 | 6.4 GB | High | C40 |
Q8_0 | 8 | 8.3 GB | Very High | C40 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C42 |
Not always. Mac Studio M3 Ultra 96GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.